first commit

.venv/Lib/site-packages/altair/vegalite/v4/__init__.py

@@ -0,0 +1,23 @@
+# flake8: noqa
+from .schema import *
+from .api import *
+
+from ...datasets import list_datasets, load_dataset
+
+from ... import expr
+from ...expr import datum
+
+from .display import VegaLite, renderers
+
+from .data import (
+    MaxRowsError,
+    pipe,
+    curry,
+    limit_rows,
+    sample,
+    to_json,
+    to_csv,
+    to_values,
+    default_data_transformer,
+    data_transformers,
+)

.venv/Lib/site-packages/altair/vegalite/v4/data.py

@@ -0,0 +1,43 @@
+from ..data import (
+    MaxRowsError,
+    curry,
+    default_data_transformer,
+    limit_rows,
+    pipe,
+    sample,
+    to_csv,
+    to_json,
+    to_values,
+    DataTransformerRegistry,
+)
+
+
+# ==============================================================================
+# VegaLite 4 data transformers
+# ==============================================================================
+
+
+ENTRY_POINT_GROUP = "altair.vegalite.v4.data_transformer"  # type: str
+
+
+data_transformers = DataTransformerRegistry(
+    entry_point_group=ENTRY_POINT_GROUP
+)  # type: DataTransformerRegistry
+data_transformers.register("default", default_data_transformer)
+data_transformers.register("json", to_json)
+data_transformers.register("csv", to_csv)
+data_transformers.enable("default")
+
+
+__all__ = (
+    "MaxRowsError",
+    "curry",
+    "default_data_transformer",
+    "limit_rows",
+    "pipe",
+    "sample",
+    "to_csv",
+    "to_json",
+    "to_values",
+    "data_transformers",
+)

.venv/Lib/site-packages/altair/vegalite/v4/display.py

@@ -0,0 +1,119 @@
+import os
+
+from ...utils.mimebundle import spec_to_mimebundle
+from ..display import Displayable
+from ..display import default_renderer_base
+from ..display import json_renderer_base
+from ..display import RendererRegistry
+from ..display import HTMLRenderer
+
+from .schema import SCHEMA_VERSION
+
+VEGALITE_VERSION = SCHEMA_VERSION.lstrip("v")
+VEGA_VERSION = "5"
+VEGAEMBED_VERSION = "6"
+
+
+# ==============================================================================
+# VegaLite v4 renderer logic
+# ==============================================================================
+
+
+# The MIME type for Vega-Lite 4.x releases.
+VEGALITE_MIME_TYPE = "application/vnd.vegalite.v4+json"  # type: str
+
+# The entry point group that can be used by other packages to declare other
+# renderers that will be auto-detected. Explicit registration is also
+# allowed by the PluginRegistery API.
+ENTRY_POINT_GROUP = "altair.vegalite.v4.renderer"  # type: str
+
+# The display message when rendering fails
+DEFAULT_DISPLAY = """\
+<VegaLite 4 object>
+
+If you see this message, it means the renderer has not been properly enabled
+for the frontend that you are using. For more information, see
+https://altair-viz.github.io/user_guide/troubleshooting.html
+"""
+
+renderers = RendererRegistry(entry_point_group=ENTRY_POINT_GROUP)
+
+here = os.path.dirname(os.path.realpath(__file__))
+
+
+def mimetype_renderer(spec, **metadata):
+    return default_renderer_base(spec, VEGALITE_MIME_TYPE, DEFAULT_DISPLAY, **metadata)
+
+
+def json_renderer(spec, **metadata):
+    return json_renderer_base(spec, DEFAULT_DISPLAY, **metadata)
+
+
+def png_renderer(spec, **metadata):
+    return spec_to_mimebundle(
+        spec,
+        format="png",
+        mode="vega-lite",
+        vega_version=VEGA_VERSION,
+        vegaembed_version=VEGAEMBED_VERSION,
+        vegalite_version=VEGALITE_VERSION,
+        **metadata,
+    )
+
+
+def svg_renderer(spec, **metadata):
+    return spec_to_mimebundle(
+        spec,
+        format="svg",
+        mode="vega-lite",
+        vega_version=VEGA_VERSION,
+        vegaembed_version=VEGAEMBED_VERSION,
+        vegalite_version=VEGALITE_VERSION,
+        **metadata,
+    )
+
+
+html_renderer = HTMLRenderer(
+    mode="vega-lite",
+    template="universal",
+    vega_version=VEGA_VERSION,
+    vegaembed_version=VEGAEMBED_VERSION,
+    vegalite_version=VEGALITE_VERSION,
+)
+
+renderers.register("default", html_renderer)
+renderers.register("html", html_renderer)
+renderers.register("colab", html_renderer)
+renderers.register("kaggle", html_renderer)
+renderers.register("zeppelin", html_renderer)
+renderers.register("mimetype", mimetype_renderer)
+renderers.register("jupyterlab", mimetype_renderer)
+renderers.register("nteract", mimetype_renderer)
+renderers.register("json", json_renderer)
+renderers.register("png", png_renderer)
+renderers.register("svg", svg_renderer)
+renderers.enable("default")
+
+
+class VegaLite(Displayable):
+    """An IPython/Jupyter display class for rendering VegaLite 4."""
+
+    renderers = renderers
+    schema_path = (__name__, "schema/vega-lite-schema.json")
+
+
+def vegalite(spec, validate=True):
+    """Render and optionally validate a VegaLite 4 spec.
+
+    This will use the currently enabled renderer to render the spec.
+
+    Parameters
+    ==========
+    spec: dict
+        A fully compliant VegaLite 4 spec, with the data portion fully processed.
+    validate: bool
+        Should the spec be validated against the VegaLite 4 schema?
+    """
+    from IPython.display import display
+
+    display(VegaLite(spec, validate=validate))

.venv/Lib/site-packages/altair/vegalite/v4/schema/__init__.py

@@ -0,0 +1,5 @@
+# flake8: noqa
+from .core import *
+from .channels import *
+SCHEMA_VERSION = 'v4.17.0'
+SCHEMA_URL = 'https://vega.github.io/schema/vega-lite/v4.17.0.json'

.venv/Lib/site-packages/altair/vegalite/v4/tests/test_api.py

@@ -0,0 +1,958 @@
+"""Unit tests for altair API"""
+
+import io
+import json
+import operator
+import os
+import pathlib
+import tempfile
+
+import jsonschema
+import pytest
+import pandas as pd
+
+import altair.vegalite.v4 as alt
+
+try:
+    import altair_saver  # noqa: F401
+except ImportError:
+    altair_saver = None
+
+
+def getargs(*args, **kwargs):
+    return args, kwargs
+
+
+OP_DICT = {
+    "layer": operator.add,
+    "hconcat": operator.or_,
+    "vconcat": operator.and_,
+}
+
+
+def _make_chart_type(chart_type):
+    data = pd.DataFrame(
+        {
+            "x": [28, 55, 43, 91, 81, 53, 19, 87],
+            "y": [43, 91, 81, 53, 19, 87, 52, 28],
+            "color": list("AAAABBBB"),
+        }
+    )
+    base = (
+        alt.Chart(data)
+        .mark_point()
+        .encode(
+            x="x",
+            y="y",
+            color="color",
+        )
+    )
+
+    if chart_type in ["layer", "hconcat", "vconcat", "concat"]:
+        func = getattr(alt, chart_type)
+        return func(base.mark_square(), base.mark_circle())
+    elif chart_type == "facet":
+        return base.facet("color")
+    elif chart_type == "facet_encoding":
+        return base.encode(facet="color")
+    elif chart_type == "repeat":
+        return base.encode(alt.X(alt.repeat(), type="quantitative")).repeat(["x", "y"])
+    elif chart_type == "chart":
+        return base
+    else:
+        raise ValueError("chart_type='{}' is not recognized".format(chart_type))
+
+
+@pytest.fixture
+def basic_chart():
+    data = pd.DataFrame(
+        {
+            "a": ["A", "B", "C", "D", "E", "F", "G", "H", "I"],
+            "b": [28, 55, 43, 91, 81, 53, 19, 87, 52],
+        }
+    )
+
+    return alt.Chart(data).mark_bar().encode(x="a", y="b")
+
+
+def test_chart_data_types():
+    def Chart(data):
+        return alt.Chart(data).mark_point().encode(x="x:Q", y="y:Q")
+
+    # Url Data
+    data = "/path/to/my/data.csv"
+    dct = Chart(data).to_dict()
+    assert dct["data"] == {"url": data}
+
+    # Dict Data
+    data = {"values": [{"x": 1, "y": 2}, {"x": 2, "y": 3}]}
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        dct = Chart(data).to_dict()
+    assert dct["data"] == data
+
+    with alt.data_transformers.enable(consolidate_datasets=True):
+        dct = Chart(data).to_dict()
+    name = dct["data"]["name"]
+    assert dct["datasets"][name] == data["values"]
+
+    # DataFrame data
+    data = pd.DataFrame({"x": range(5), "y": range(5)})
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        dct = Chart(data).to_dict()
+    assert dct["data"]["values"] == data.to_dict(orient="records")
+
+    with alt.data_transformers.enable(consolidate_datasets=True):
+        dct = Chart(data).to_dict()
+    name = dct["data"]["name"]
+    assert dct["datasets"][name] == data.to_dict(orient="records")
+
+    # Named data object
+    data = alt.NamedData(name="Foo")
+    dct = Chart(data).to_dict()
+    assert dct["data"] == {"name": "Foo"}
+
+
+def test_chart_infer_types():
+    data = pd.DataFrame(
+        {
+            "x": pd.date_range("2012", periods=10, freq="Y"),
+            "y": range(10),
+            "c": list("abcabcabca"),
+        }
+    )
+
+    def _check_encodings(chart):
+        dct = chart.to_dict()
+        assert dct["encoding"]["x"]["type"] == "temporal"
+        assert dct["encoding"]["x"]["field"] == "x"
+        assert dct["encoding"]["y"]["type"] == "quantitative"
+        assert dct["encoding"]["y"]["field"] == "y"
+        assert dct["encoding"]["color"]["type"] == "nominal"
+        assert dct["encoding"]["color"]["field"] == "c"
+
+    # Pass field names by keyword
+    chart = alt.Chart(data).mark_point().encode(x="x", y="y", color="c")
+    _check_encodings(chart)
+
+    # pass Channel objects by keyword
+    chart = (
+        alt.Chart(data)
+        .mark_point()
+        .encode(x=alt.X("x"), y=alt.Y("y"), color=alt.Color("c"))
+    )
+    _check_encodings(chart)
+
+    # pass Channel objects by value
+    chart = alt.Chart(data).mark_point().encode(alt.X("x"), alt.Y("y"), alt.Color("c"))
+    _check_encodings(chart)
+
+    # override default types
+    chart = (
+        alt.Chart(data)
+        .mark_point()
+        .encode(alt.X("x", type="nominal"), alt.Y("y", type="ordinal"))
+    )
+    dct = chart.to_dict()
+    assert dct["encoding"]["x"]["type"] == "nominal"
+    assert dct["encoding"]["y"]["type"] == "ordinal"
+
+
+@pytest.mark.parametrize(
+    "args, kwargs",
+    [
+        getargs(detail=["value:Q", "name:N"], tooltip=["value:Q", "name:N"]),
+        getargs(detail=["value", "name"], tooltip=["value", "name"]),
+        getargs(alt.Detail(["value:Q", "name:N"]), alt.Tooltip(["value:Q", "name:N"])),
+        getargs(alt.Detail(["value", "name"]), alt.Tooltip(["value", "name"])),
+        getargs(
+            [alt.Detail("value:Q"), alt.Detail("name:N")],
+            [alt.Tooltip("value:Q"), alt.Tooltip("name:N")],
+        ),
+        getargs(
+            [alt.Detail("value"), alt.Detail("name")],
+            [alt.Tooltip("value"), alt.Tooltip("name")],
+        ),
+    ],
+)
+def test_multiple_encodings(args, kwargs):
+    df = pd.DataFrame({"value": [1, 2, 3], "name": ["A", "B", "C"]})
+    encoding_dct = [
+        {"field": "value", "type": "quantitative"},
+        {"field": "name", "type": "nominal"},
+    ]
+    chart = alt.Chart(df).mark_point().encode(*args, **kwargs)
+    dct = chart.to_dict()
+    assert dct["encoding"]["detail"] == encoding_dct
+    assert dct["encoding"]["tooltip"] == encoding_dct
+
+
+def test_chart_operations():
+    data = pd.DataFrame(
+        {
+            "x": pd.date_range("2012", periods=10, freq="Y"),
+            "y": range(10),
+            "c": list("abcabcabca"),
+        }
+    )
+    chart1 = alt.Chart(data).mark_line().encode(x="x", y="y", color="c")
+    chart2 = chart1.mark_point()
+    chart3 = chart1.mark_circle()
+    chart4 = chart1.mark_square()
+
+    chart = chart1 + chart2 + chart3
+    assert isinstance(chart, alt.LayerChart)
+    assert len(chart.layer) == 3
+    chart += chart4
+    assert len(chart.layer) == 4
+
+    chart = chart1 | chart2 | chart3
+    assert isinstance(chart, alt.HConcatChart)
+    assert len(chart.hconcat) == 3
+    chart |= chart4
+    assert len(chart.hconcat) == 4
+
+    chart = chart1 & chart2 & chart3
+    assert isinstance(chart, alt.VConcatChart)
+    assert len(chart.vconcat) == 3
+    chart &= chart4
+    assert len(chart.vconcat) == 4
+
+
+def test_selection_to_dict():
+    brush = alt.selection(type="interval")
+
+    # test some value selections
+    # Note: X and Y cannot have conditions
+    alt.Chart("path/to/data.json").mark_point().encode(
+        color=alt.condition(brush, alt.ColorValue("red"), alt.ColorValue("blue")),
+        opacity=alt.condition(brush, alt.value(0.5), alt.value(1.0)),
+        text=alt.condition(brush, alt.TextValue("foo"), alt.value("bar")),
+    ).to_dict()
+
+    # test some field selections
+    # Note: X and Y cannot have conditions
+    # Conditions cannot both be fields
+    alt.Chart("path/to/data.json").mark_point().encode(
+        color=alt.condition(brush, alt.Color("col1:N"), alt.value("blue")),
+        opacity=alt.condition(brush, "col1:N", alt.value(0.5)),
+        text=alt.condition(brush, alt.value("abc"), alt.Text("col2:N")),
+        size=alt.condition(brush, alt.value(20), "col2:N"),
+    ).to_dict()
+
+
+def test_selection_expression():
+    selection = alt.selection_single(fields=["value"])
+
+    assert isinstance(selection.value, alt.expr.Expression)
+    assert selection.value.to_dict() == "{0}.value".format(selection.name)
+
+    assert isinstance(selection["value"], alt.expr.Expression)
+    assert selection["value"].to_dict() == "{0}['value']".format(selection.name)
+
+    with pytest.raises(AttributeError):
+        selection.__magic__
+
+
+@pytest.mark.parametrize("format", ["html", "json", "png", "svg", "pdf"])
+def test_save(format, basic_chart):
+    if format in ["pdf", "png"]:
+        out = io.BytesIO()
+        mode = "rb"
+    else:
+        out = io.StringIO()
+        mode = "r"
+
+    if format in ["svg", "png", "pdf"]:
+        if not altair_saver:
+            with pytest.raises(ValueError) as err:
+                basic_chart.save(out, format=format)
+            assert "github.com/altair-viz/altair_saver" in str(err.value)
+            return
+        elif format not in altair_saver.available_formats():
+            with pytest.raises(ValueError) as err:
+                basic_chart.save(out, format=format)
+            assert f"No enabled saver found that supports format='{format}'" in str(
+                err.value
+            )
+            return
+
+    basic_chart.save(out, format=format)
+    out.seek(0)
+    content = out.read()
+
+    if format == "json":
+        assert "$schema" in json.loads(content)
+    if format == "html":
+        assert content.startswith("<!DOCTYPE html>")
+
+    fid, filename = tempfile.mkstemp(suffix="." + format)
+    os.close(fid)
+
+    # test both string filenames and pathlib.Paths
+    for fp in [filename, pathlib.Path(filename)]:
+        try:
+            basic_chart.save(fp)
+            with open(fp, mode) as f:
+                assert f.read()[:1000] == content[:1000]
+        finally:
+            os.remove(fp)
+
+
+def test_facet_basic():
+    # wrapped facet
+    chart1 = (
+        alt.Chart("data.csv")
+        .mark_point()
+        .encode(
+            x="x:Q",
+            y="y:Q",
+        )
+        .facet("category:N", columns=2)
+    )
+
+    dct1 = chart1.to_dict()
+
+    assert dct1["facet"] == alt.Facet("category:N").to_dict()
+    assert dct1["columns"] == 2
+    assert dct1["data"] == alt.UrlData("data.csv").to_dict()
+
+    # explicit row/col facet
+    chart2 = (
+        alt.Chart("data.csv")
+        .mark_point()
+        .encode(
+            x="x:Q",
+            y="y:Q",
+        )
+        .facet(row="category1:Q", column="category2:Q")
+    )
+
+    dct2 = chart2.to_dict()
+
+    assert dct2["facet"]["row"] == alt.Facet("category1:Q").to_dict()
+    assert dct2["facet"]["column"] == alt.Facet("category2:Q").to_dict()
+    assert "columns" not in dct2
+    assert dct2["data"] == alt.UrlData("data.csv").to_dict()
+
+
+def test_facet_parse():
+    chart = (
+        alt.Chart("data.csv")
+        .mark_point()
+        .encode(x="x:Q", y="y:Q")
+        .facet(row="row:N", column="column:O")
+    )
+    dct = chart.to_dict()
+    assert dct["data"] == {"url": "data.csv"}
+    assert "data" not in dct["spec"]
+    assert dct["facet"] == {
+        "column": {"field": "column", "type": "ordinal"},
+        "row": {"field": "row", "type": "nominal"},
+    }
+
+
+def test_facet_parse_data():
+    data = pd.DataFrame({"x": range(5), "y": range(5), "row": list("abcab")})
+    chart = (
+        alt.Chart(data)
+        .mark_point()
+        .encode(x="x", y="y:O")
+        .facet(row="row", column="column:O")
+    )
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        dct = chart.to_dict()
+    assert "values" in dct["data"]
+    assert "data" not in dct["spec"]
+    assert dct["facet"] == {
+        "column": {"field": "column", "type": "ordinal"},
+        "row": {"field": "row", "type": "nominal"},
+    }
+
+    with alt.data_transformers.enable(consolidate_datasets=True):
+        dct = chart.to_dict()
+    assert "datasets" in dct
+    assert "name" in dct["data"]
+    assert "data" not in dct["spec"]
+    assert dct["facet"] == {
+        "column": {"field": "column", "type": "ordinal"},
+        "row": {"field": "row", "type": "nominal"},
+    }
+
+
+def test_selection():
+    # test instantiation of selections
+    interval = alt.selection_interval(name="selec_1")
+    assert interval.selection.type == "interval"
+    assert interval.name == "selec_1"
+
+    single = alt.selection_single(name="selec_2")
+    assert single.selection.type == "single"
+    assert single.name == "selec_2"
+
+    multi = alt.selection_multi(name="selec_3")
+    assert multi.selection.type == "multi"
+    assert multi.name == "selec_3"
+
+    # test adding to chart
+    chart = alt.Chart().add_selection(single)
+    chart = chart.add_selection(multi, interval)
+    assert set(chart.selection.keys()) == {"selec_1", "selec_2", "selec_3"}
+
+    # test logical operations
+    assert isinstance(single & multi, alt.Selection)
+    assert isinstance(single | multi, alt.Selection)
+    assert isinstance(~single, alt.Selection)
+    assert isinstance((single & multi)[0].group, alt.SelectionAnd)
+    assert isinstance((single | multi)[0].group, alt.SelectionOr)
+    assert isinstance((~single)[0].group, alt.SelectionNot)
+
+    # test that default names increment (regression for #1454)
+    sel1 = alt.selection_single()
+    sel2 = alt.selection_multi()
+    sel3 = alt.selection_interval()
+    names = {s.name for s in (sel1, sel2, sel3)}
+    assert len(names) == 3
+
+
+def test_transforms():
+    # aggregate transform
+    agg1 = alt.AggregatedFieldDef(**{"as": "x1", "op": "mean", "field": "y"})
+    agg2 = alt.AggregatedFieldDef(**{"as": "x2", "op": "median", "field": "z"})
+    chart = alt.Chart().transform_aggregate([agg1], ["foo"], x2="median(z)")
+    kwds = dict(aggregate=[agg1, agg2], groupby=["foo"])
+    assert chart.transform == [alt.AggregateTransform(**kwds)]
+
+    # bin transform
+    chart = alt.Chart().transform_bin("binned", field="field", bin=True)
+    kwds = {"as": "binned", "field": "field", "bin": True}
+    assert chart.transform == [alt.BinTransform(**kwds)]
+
+    # calcualte transform
+    chart = alt.Chart().transform_calculate("calc", "datum.a * 4")
+    kwds = {"as": "calc", "calculate": "datum.a * 4"}
+    assert chart.transform == [alt.CalculateTransform(**kwds)]
+
+    # density transform
+    chart = alt.Chart().transform_density("x", as_=["value", "density"])
+    kwds = {"as": ["value", "density"], "density": "x"}
+    assert chart.transform == [alt.DensityTransform(**kwds)]
+
+    # filter transform
+    chart = alt.Chart().transform_filter("datum.a < 4")
+    assert chart.transform == [alt.FilterTransform(filter="datum.a < 4")]
+
+    # flatten transform
+    chart = alt.Chart().transform_flatten(["A", "B"], ["X", "Y"])
+    kwds = {"as": ["X", "Y"], "flatten": ["A", "B"]}
+    assert chart.transform == [alt.FlattenTransform(**kwds)]
+
+    # fold transform
+    chart = alt.Chart().transform_fold(["A", "B", "C"], as_=["key", "val"])
+    kwds = {"as": ["key", "val"], "fold": ["A", "B", "C"]}
+    assert chart.transform == [alt.FoldTransform(**kwds)]
+
+    # impute transform
+    chart = alt.Chart().transform_impute("field", "key", groupby=["x"])
+    kwds = {"impute": "field", "key": "key", "groupby": ["x"]}
+    assert chart.transform == [alt.ImputeTransform(**kwds)]
+
+    # joinaggregate transform
+    chart = alt.Chart().transform_joinaggregate(min="min(x)", groupby=["key"])
+    kwds = {
+        "joinaggregate": [
+            alt.JoinAggregateFieldDef(field="x", op="min", **{"as": "min"})
+        ],
+        "groupby": ["key"],
+    }
+    assert chart.transform == [alt.JoinAggregateTransform(**kwds)]
+
+    # loess transform
+    chart = alt.Chart().transform_loess("x", "y", as_=["xx", "yy"])
+    kwds = {"on": "x", "loess": "y", "as": ["xx", "yy"]}
+    assert chart.transform == [alt.LoessTransform(**kwds)]
+
+    # lookup transform (data)
+    lookup_data = alt.LookupData(alt.UrlData("foo.csv"), "id", ["rate"])
+    chart = alt.Chart().transform_lookup("a", from_=lookup_data, as_="a", default="b")
+    kwds = {"from": lookup_data, "as": "a", "lookup": "a", "default": "b"}
+    assert chart.transform == [alt.LookupTransform(**kwds)]
+
+    # lookup transform (selection)
+    lookup_selection = alt.LookupSelection(key="key", selection="sel")
+    chart = alt.Chart().transform_lookup(
+        "a", from_=lookup_selection, as_="a", default="b"
+    )
+    kwds = {"from": lookup_selection, "as": "a", "lookup": "a", "default": "b"}
+    assert chart.transform == [alt.LookupTransform(**kwds)]
+
+    # pivot transform
+    chart = alt.Chart().transform_pivot("x", "y")
+    assert chart.transform == [alt.PivotTransform(pivot="x", value="y")]
+
+    # quantile transform
+    chart = alt.Chart().transform_quantile("x", as_=["prob", "value"])
+    kwds = {"quantile": "x", "as": ["prob", "value"]}
+    assert chart.transform == [alt.QuantileTransform(**kwds)]
+
+    # regression transform
+    chart = alt.Chart().transform_regression("x", "y", as_=["xx", "yy"])
+    kwds = {"on": "x", "regression": "y", "as": ["xx", "yy"]}
+    assert chart.transform == [alt.RegressionTransform(**kwds)]
+
+    # sample transform
+    chart = alt.Chart().transform_sample()
+    assert chart.transform == [alt.SampleTransform(1000)]
+
+    # stack transform
+    chart = alt.Chart().transform_stack("stacked", "x", groupby=["y"])
+    assert chart.transform == [
+        alt.StackTransform(stack="x", groupby=["y"], **{"as": "stacked"})
+    ]
+
+    # timeUnit transform
+    chart = alt.Chart().transform_timeunit("foo", field="x", timeUnit="date")
+    kwds = {"as": "foo", "field": "x", "timeUnit": "date"}
+    assert chart.transform == [alt.TimeUnitTransform(**kwds)]
+
+    # window transform
+    chart = alt.Chart().transform_window(xsum="sum(x)", ymin="min(y)", frame=[None, 0])
+    window = [
+        alt.WindowFieldDef(**{"as": "xsum", "field": "x", "op": "sum"}),
+        alt.WindowFieldDef(**{"as": "ymin", "field": "y", "op": "min"}),
+    ]
+
+    # kwargs don't maintain order in Python < 3.6, so window list can
+    # be reversed
+    assert chart.transform == [
+        alt.WindowTransform(frame=[None, 0], window=window)
+    ] or chart.transform == [alt.WindowTransform(frame=[None, 0], window=window[::-1])]
+
+
+def test_filter_transform_selection_predicates():
+    selector1 = alt.selection_interval(name="s1")
+    selector2 = alt.selection_interval(name="s2")
+    base = alt.Chart("data.txt").mark_point()
+
+    chart = base.transform_filter(selector1)
+    assert chart.to_dict()["transform"] == [{"filter": {"selection": "s1"}}]
+
+    chart = base.transform_filter(~selector1)
+    assert chart.to_dict()["transform"] == [{"filter": {"selection": {"not": "s1"}}}]
+
+    chart = base.transform_filter(selector1 & selector2)
+    assert chart.to_dict()["transform"] == [
+        {"filter": {"selection": {"and": ["s1", "s2"]}}}
+    ]
+
+    chart = base.transform_filter(selector1 | selector2)
+    assert chart.to_dict()["transform"] == [
+        {"filter": {"selection": {"or": ["s1", "s2"]}}}
+    ]
+
+    chart = base.transform_filter(selector1 | ~selector2)
+    assert chart.to_dict()["transform"] == [
+        {"filter": {"selection": {"or": ["s1", {"not": "s2"}]}}}
+    ]
+
+    chart = base.transform_filter(~selector1 | ~selector2)
+    assert chart.to_dict()["transform"] == [
+        {"filter": {"selection": {"or": [{"not": "s1"}, {"not": "s2"}]}}}
+    ]
+
+    chart = base.transform_filter(~(selector1 & selector2))
+    assert chart.to_dict()["transform"] == [
+        {"filter": {"selection": {"not": {"and": ["s1", "s2"]}}}}
+    ]
+
+
+def test_resolve_methods():
+    chart = alt.LayerChart().resolve_axis(x="shared", y="independent")
+    assert chart.resolve == alt.Resolve(
+        axis=alt.AxisResolveMap(x="shared", y="independent")
+    )
+
+    chart = alt.LayerChart().resolve_legend(color="shared", fill="independent")
+    assert chart.resolve == alt.Resolve(
+        legend=alt.LegendResolveMap(color="shared", fill="independent")
+    )
+
+    chart = alt.LayerChart().resolve_scale(x="shared", y="independent")
+    assert chart.resolve == alt.Resolve(
+        scale=alt.ScaleResolveMap(x="shared", y="independent")
+    )
+
+
+def test_layer_encodings():
+    chart = alt.LayerChart().encode(x="column:Q")
+    assert chart.encoding.x == alt.X(shorthand="column:Q")
+
+
+def test_add_selection():
+    selections = [
+        alt.selection_interval(),
+        alt.selection_single(),
+        alt.selection_multi(),
+    ]
+    chart = (
+        alt.Chart()
+        .mark_point()
+        .add_selection(selections[0])
+        .add_selection(selections[1], selections[2])
+    )
+    expected = {s.name: s.selection for s in selections}
+    assert chart.selection == expected
+
+
+def test_repeat_add_selections():
+    base = alt.Chart("data.csv").mark_point()
+    selection = alt.selection_single()
+    chart1 = base.add_selection(selection).repeat(list("ABC"))
+    chart2 = base.repeat(list("ABC")).add_selection(selection)
+    assert chart1.to_dict() == chart2.to_dict()
+
+
+def test_facet_add_selections():
+    base = alt.Chart("data.csv").mark_point()
+    selection = alt.selection_single()
+    chart1 = base.add_selection(selection).facet("val:Q")
+    chart2 = base.facet("val:Q").add_selection(selection)
+    assert chart1.to_dict() == chart2.to_dict()
+
+
+def test_layer_add_selection():
+    base = alt.Chart("data.csv").mark_point()
+    selection = alt.selection_single()
+    chart1 = alt.layer(base.add_selection(selection), base)
+    chart2 = alt.layer(base, base).add_selection(selection)
+    assert chart1.to_dict() == chart2.to_dict()
+
+
+@pytest.mark.parametrize("charttype", [alt.concat, alt.hconcat, alt.vconcat])
+def test_compound_add_selections(charttype):
+    base = alt.Chart("data.csv").mark_point()
+    selection = alt.selection_single()
+    chart1 = charttype(base.add_selection(selection), base.add_selection(selection))
+    chart2 = charttype(base, base).add_selection(selection)
+    assert chart1.to_dict() == chart2.to_dict()
+
+
+def test_selection_property():
+    sel = alt.selection_interval()
+    chart = alt.Chart("data.csv").mark_point().properties(selection=sel)
+
+    assert list(chart["selection"].keys()) == [sel.name]
+
+
+def test_LookupData():
+    df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    lookup = alt.LookupData(data=df, key="x")
+
+    dct = lookup.to_dict()
+    assert dct["key"] == "x"
+    assert dct["data"] == {
+        "values": [{"x": 1, "y": 4}, {"x": 2, "y": 5}, {"x": 3, "y": 6}]
+    }
+
+
+def test_themes():
+    chart = alt.Chart("foo.txt").mark_point()
+
+    with alt.themes.enable("default"):
+        assert chart.to_dict()["config"] == {
+            "view": {"continuousWidth": 400, "continuousHeight": 300}
+        }
+
+    with alt.themes.enable("opaque"):
+        assert chart.to_dict()["config"] == {
+            "background": "white",
+            "view": {"continuousWidth": 400, "continuousHeight": 300},
+        }
+
+    with alt.themes.enable("none"):
+        assert "config" not in chart.to_dict()
+
+
+def test_chart_from_dict():
+    base = alt.Chart("data.csv").mark_point().encode(x="x:Q", y="y:Q")
+
+    charts = [
+        base,
+        base + base,
+        base | base,
+        base & base,
+        base.facet("c:N"),
+        (base + base).facet(row="c:N", data="data.csv"),
+        base.repeat(["c", "d"]),
+        (base + base).repeat(row=["c", "d"]),
+    ]
+
+    for chart in charts:
+        chart_out = alt.Chart.from_dict(chart.to_dict())
+        assert type(chart_out) is type(chart)
+
+    # test that an invalid spec leads to a schema validation error
+    with pytest.raises(jsonschema.ValidationError):
+        alt.Chart.from_dict({"invalid": "spec"})
+
+
+def test_consolidate_datasets(basic_chart):
+    subchart1 = basic_chart
+    subchart2 = basic_chart.copy()
+    subchart2.data = basic_chart.data.copy()
+    chart = subchart1 | subchart2
+
+    with alt.data_transformers.enable(consolidate_datasets=True):
+        dct_consolidated = chart.to_dict()
+
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        dct_standard = chart.to_dict()
+
+    assert "datasets" in dct_consolidated
+    assert "datasets" not in dct_standard
+
+    datasets = dct_consolidated["datasets"]
+
+    # two dataset copies should be recognized as duplicates
+    assert len(datasets) == 1
+
+    # make sure data matches original & names are correct
+    name, data = datasets.popitem()
+
+    for spec in dct_standard["hconcat"]:
+        assert spec["data"]["values"] == data
+
+    for spec in dct_consolidated["hconcat"]:
+        assert spec["data"] == {"name": name}
+
+
+def test_consolidate_InlineData():
+    data = alt.InlineData(
+        values=[{"a": 1, "b": 1}, {"a": 2, "b": 2}], format={"type": "csv"}
+    )
+    chart = alt.Chart(data).mark_point()
+
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        dct = chart.to_dict()
+    assert dct["data"]["format"] == data.format
+    assert dct["data"]["values"] == data.values
+
+    with alt.data_transformers.enable(consolidate_datasets=True):
+        dct = chart.to_dict()
+    assert dct["data"]["format"] == data.format
+    assert list(dct["datasets"].values())[0] == data.values
+
+    data = alt.InlineData(values=[], name="runtime_data")
+    chart = alt.Chart(data).mark_point()
+
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        dct = chart.to_dict()
+    assert dct["data"] == data.to_dict()
+
+    with alt.data_transformers.enable(consolidate_datasets=True):
+        dct = chart.to_dict()
+    assert dct["data"] == data.to_dict()
+
+
+def test_repeat():
+    # wrapped repeat
+    chart1 = (
+        alt.Chart("data.csv")
+        .mark_point()
+        .encode(
+            x=alt.X(alt.repeat(), type="quantitative"),
+            y="y:Q",
+        )
+        .repeat(["A", "B", "C", "D"], columns=2)
+    )
+
+    dct1 = chart1.to_dict()
+
+    assert dct1["repeat"] == ["A", "B", "C", "D"]
+    assert dct1["columns"] == 2
+    assert dct1["spec"]["encoding"]["x"]["field"] == {"repeat": "repeat"}
+
+    # explicit row/col repeat
+    chart2 = (
+        alt.Chart("data.csv")
+        .mark_point()
+        .encode(
+            x=alt.X(alt.repeat("row"), type="quantitative"),
+            y=alt.Y(alt.repeat("column"), type="quantitative"),
+        )
+        .repeat(row=["A", "B", "C"], column=["C", "B", "A"])
+    )
+
+    dct2 = chart2.to_dict()
+
+    assert dct2["repeat"] == {"row": ["A", "B", "C"], "column": ["C", "B", "A"]}
+    assert "columns" not in dct2
+    assert dct2["spec"]["encoding"]["x"]["field"] == {"repeat": "row"}
+    assert dct2["spec"]["encoding"]["y"]["field"] == {"repeat": "column"}
+
+
+def test_data_property():
+    data = pd.DataFrame({"x": [1, 2, 3], "y": list("ABC")})
+    chart1 = alt.Chart(data).mark_point()
+    chart2 = alt.Chart().mark_point().properties(data=data)
+
+    assert chart1.to_dict() == chart2.to_dict()
+
+
+@pytest.mark.parametrize("method", ["layer", "hconcat", "vconcat", "concat"])
+@pytest.mark.parametrize(
+    "data", ["data.json", pd.DataFrame({"x": range(3), "y": list("abc")})]
+)
+def test_subcharts_with_same_data(method, data):
+    func = getattr(alt, method)
+
+    point = alt.Chart(data).mark_point().encode(x="x:Q", y="y:Q")
+    line = point.mark_line()
+    text = point.mark_text()
+
+    chart1 = func(point, line, text)
+    assert chart1.data is not alt.Undefined
+    assert all(c.data is alt.Undefined for c in getattr(chart1, method))
+
+    if method != "concat":
+        op = OP_DICT[method]
+        chart2 = op(op(point, line), text)
+        assert chart2.data is not alt.Undefined
+        assert all(c.data is alt.Undefined for c in getattr(chart2, method))
+
+
+@pytest.mark.parametrize("method", ["layer", "hconcat", "vconcat", "concat"])
+@pytest.mark.parametrize(
+    "data", ["data.json", pd.DataFrame({"x": range(3), "y": list("abc")})]
+)
+def test_subcharts_different_data(method, data):
+    func = getattr(alt, method)
+
+    point = alt.Chart(data).mark_point().encode(x="x:Q", y="y:Q")
+    otherdata = alt.Chart("data.csv").mark_point().encode(x="x:Q", y="y:Q")
+    nodata = alt.Chart().mark_point().encode(x="x:Q", y="y:Q")
+
+    chart1 = func(point, otherdata)
+    assert chart1.data is alt.Undefined
+    assert getattr(chart1, method)[0].data is data
+
+    chart2 = func(point, nodata)
+    assert chart2.data is alt.Undefined
+    assert getattr(chart2, method)[0].data is data
+
+
+def test_layer_facet(basic_chart):
+    chart = (basic_chart + basic_chart).facet(row="row:Q")
+    assert chart.data is not alt.Undefined
+    assert chart.spec.data is alt.Undefined
+    for layer in chart.spec.layer:
+        assert layer.data is alt.Undefined
+
+    dct = chart.to_dict()
+    assert "data" in dct
+
+
+def test_layer_errors():
+    toplevel_chart = alt.Chart("data.txt").mark_point().configure_legend(columns=2)
+
+    facet_chart1 = alt.Chart("data.txt").mark_point().encode(facet="row:Q")
+
+    facet_chart2 = alt.Chart("data.txt").mark_point().facet("row:Q")
+
+    repeat_chart = alt.Chart("data.txt").mark_point().repeat(["A", "B", "C"])
+
+    simple_chart = alt.Chart("data.txt").mark_point()
+
+    with pytest.raises(ValueError) as err:
+        toplevel_chart + simple_chart
+    assert str(err.value).startswith(
+        'Objects with "config" attribute cannot be used within LayerChart.'
+    )
+
+    with pytest.raises(ValueError) as err:
+        repeat_chart + simple_chart
+    assert str(err.value) == "Repeat charts cannot be layered."
+
+    with pytest.raises(ValueError) as err:
+        facet_chart1 + simple_chart
+    assert str(err.value) == "Faceted charts cannot be layered."
+
+    with pytest.raises(ValueError) as err:
+        alt.layer(simple_chart) + facet_chart2
+    assert str(err.value) == "Faceted charts cannot be layered."
+
+
+@pytest.mark.parametrize(
+    "chart_type",
+    ["layer", "hconcat", "vconcat", "concat", "facet", "facet_encoding", "repeat"],
+)
+def test_resolve(chart_type):
+    chart = _make_chart_type(chart_type)
+    chart = (
+        chart.resolve_scale(
+            x="independent",
+        )
+        .resolve_legend(color="independent")
+        .resolve_axis(y="independent")
+    )
+    dct = chart.to_dict()
+    assert dct["resolve"] == {
+        "scale": {"x": "independent"},
+        "legend": {"color": "independent"},
+        "axis": {"y": "independent"},
+    }
+
+
+# TODO: test vconcat, hconcat, concat, facet_encoding when schema allows them.
+# This is blocked by https://github.com/vega/vega-lite/issues/5261
+@pytest.mark.parametrize("chart_type", ["chart", "layer"])
+@pytest.mark.parametrize("facet_arg", [None, "facet", "row", "column"])
+def test_facet(chart_type, facet_arg):
+    chart = _make_chart_type(chart_type)
+    if facet_arg is None:
+        chart = chart.facet("color:N", columns=2)
+    else:
+        chart = chart.facet(**{facet_arg: "color:N", "columns": 2})
+    dct = chart.to_dict()
+
+    assert "spec" in dct
+    assert dct["columns"] == 2
+    expected = {"field": "color", "type": "nominal"}
+    if facet_arg is None or facet_arg == "facet":
+        assert dct["facet"] == expected
+    else:
+        assert dct["facet"][facet_arg] == expected
+
+
+def test_sequence():
+    data = alt.sequence(100)
+    assert data.to_dict() == {"sequence": {"start": 0, "stop": 100}}
+
+    data = alt.sequence(5, 10)
+    assert data.to_dict() == {"sequence": {"start": 5, "stop": 10}}
+
+    data = alt.sequence(0, 1, 0.1, as_="x")
+    assert data.to_dict() == {
+        "sequence": {"start": 0, "stop": 1, "step": 0.1, "as": "x"}
+    }
+
+
+def test_graticule():
+    data = alt.graticule()
+    assert data.to_dict() == {"graticule": True}
+
+    data = alt.graticule(step=[15, 15])
+    assert data.to_dict() == {"graticule": {"step": [15, 15]}}
+
+
+def test_sphere():
+    data = alt.sphere()
+    assert data.to_dict() == {"sphere": True}
+
+
+def test_validate_dataset():
+    d = {"data": {"values": [{}]}, "mark": {"type": "point"}}
+
+    chart = alt.Chart.from_dict(d)
+    jsn = chart.to_json()
+
+    assert jsn

.venv/Lib/site-packages/altair/vegalite/v4/tests/test_data.py

@@ -0,0 +1,33 @@
+import os
+
+import pandas as pd
+import pytest
+
+from .. import data as alt
+
+
+@pytest.fixture
+def sample_data():
+    return pd.DataFrame({"x": range(10), "y": range(10)})
+
+
+def test_disable_max_rows(sample_data):
+    with alt.data_transformers.enable("default", max_rows=5):
+        # Ensure max rows error is raised.
+        with pytest.raises(alt.MaxRowsError):
+            alt.data_transformers.get()(sample_data)
+
+        # Ensure that max rows error is properly disabled.
+        with alt.data_transformers.disable_max_rows():
+            alt.data_transformers.get()(sample_data)
+
+    try:
+        with alt.data_transformers.enable("json"):
+            # Ensure that there is no TypeError for non-max_rows transformers.
+            with alt.data_transformers.disable_max_rows():
+                jsonfile = alt.data_transformers.get()(sample_data)
+    except TypeError:
+        jsonfile = {}
+    finally:
+        if jsonfile:
+            os.remove(jsonfile["url"])

.venv/Lib/site-packages/altair/vegalite/v4/tests/test_display.py

@@ -0,0 +1,69 @@
+from contextlib import contextmanager
+
+import pytest
+
+import altair.vegalite.v4 as alt
+
+
+@contextmanager
+def check_render_options(**options):
+    """
+    Context manager that will assert that alt.renderers.options are equivalent
+    to the given options in the IPython.display.display call
+    """
+    import IPython.display
+
+    def check_options(obj):
+        assert alt.renderers.options == options
+
+    _display = IPython.display.display
+    IPython.display.display = check_options
+    try:
+        yield
+    finally:
+        IPython.display.display = _display
+
+
+def test_check_renderer_options():
+    # this test should pass
+    with check_render_options():
+        from IPython.display import display
+
+        display(None)
+
+    # check that an error is appropriately raised if the test fails
+    with pytest.raises(AssertionError):
+        with check_render_options(foo="bar"):
+            from IPython.display import display
+
+            display(None)
+
+
+def test_display_options():
+    chart = alt.Chart("data.csv").mark_point().encode(x="foo:Q")
+
+    # check that there are no options by default
+    with check_render_options():
+        chart.display()
+
+    # check that display options are passed
+    with check_render_options(embed_options={"tooltip": False, "renderer": "canvas"}):
+        chart.display("canvas", tooltip=False)
+
+    # check that above options do not persist
+    with check_render_options():
+        chart.display()
+
+    # check that display options augment rather than overwrite pre-set options
+    with alt.renderers.enable(embed_options={"tooltip": True, "renderer": "svg"}):
+        with check_render_options(embed_options={"tooltip": True, "renderer": "svg"}):
+            chart.display()
+
+        with check_render_options(
+            embed_options={"tooltip": True, "renderer": "canvas"}
+        ):
+            chart.display("canvas")
+
+    # check that above options do not persist
+    with check_render_options():
+        chart.display()

.venv/Lib/site-packages/altair/vegalite/v4/tests/test_geo_interface.py

@@ -0,0 +1,204 @@
+import pytest
+import altair.vegalite.v4 as alt
+
+
+def geom_obj(geom):
+    class Geom(object):
+        pass
+
+    geom_obj = Geom()
+    setattr(geom_obj, "__geo_interface__", geom)
+    return geom_obj
+
+
+# correct translation of Polygon geometry to Feature type
+def test_geo_interface_polygon_feature():
+    geom = {
+        "coordinates": [[(0, 0), (0, 2), (2, 2), (2, 0), (0, 0)]],
+        "type": "Polygon",
+    }
+    feat = geom_obj(geom)
+
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        spec = alt.Chart(feat).mark_geoshape().to_dict()
+    assert spec["data"]["values"]["type"] == "Feature"
+
+
+# merge geometry with empty properties dictionary
+def test_geo_interface_removal_empty_properties():
+    geom = {
+        "geometry": {
+            "coordinates": [
+                [[6.90, 53.48], [5.98, 51.85], [6.07, 53.51], [6.90, 53.48]]
+            ],
+            "type": "Polygon",
+        },
+        "id": None,
+        "properties": {},
+        "type": "Feature",
+    }
+    feat = geom_obj(geom)
+
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        spec = alt.Chart(feat).mark_geoshape().to_dict()
+    assert spec["data"]["values"]["type"] == "Feature"
+
+
+# only register metadata in the properties member
+def test_geo_interface_register_foreign_member():
+    geom = {
+        "geometry": {
+            "coordinates": [
+                [[6.90, 53.48], [5.98, 51.85], [6.07, 53.51], [6.90, 53.48]]
+            ],
+            "type": "Polygon",
+        },
+        "id": 2,
+        "properties": {"foo": "bah"},
+        "type": "Feature",
+    }
+    feat = geom_obj(geom)
+
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        spec = alt.Chart(feat).mark_geoshape().to_dict()
+    with pytest.raises(KeyError):
+        spec["data"]["values"]["id"]
+    assert spec["data"]["values"]["foo"] == "bah"
+
+
+# correct serializing of arrays and nested tuples
+def test_geo_interface_serializing_arrays_tuples():
+    import array as arr
+
+    geom = {
+        "bbox": arr.array("d", [1, 2, 3, 4]),
+        "geometry": {
+            "coordinates": [
+                tuple(
+                    (
+                        tuple((6.90, 53.48)),
+                        tuple((5.98, 51.85)),
+                        tuple((6.07, 53.51)),
+                        tuple((6.90, 53.48)),
+                    )
+                )
+            ],
+            "type": "Polygon",
+        },
+        "id": 27,
+        "properties": {},
+        "type": "Feature",
+    }
+    feat = geom_obj(geom)
+
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        spec = alt.Chart(feat).mark_geoshape().to_dict()
+    assert spec["data"]["values"]["geometry"]["coordinates"][0][0] == [6.9, 53.48]
+
+
+# overwrite existing 'type' value in properties with `Feature`
+def test_geo_interface_reserved_members():
+    geom = {
+        "geometry": {
+            "coordinates": [
+                [[6.90, 53.48], [5.98, 51.85], [6.07, 53.51], [6.90, 53.48]]
+            ],
+            "type": "Polygon",
+        },
+        "id": 27,
+        "properties": {"type": "foo"},
+        "type": "Feature",
+    }
+    feat = geom_obj(geom)
+
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        spec = alt.Chart(feat).mark_geoshape().to_dict()
+    assert spec["data"]["values"]["type"] == "Feature"
+
+
+# an empty FeatureCollection is valid
+def test_geo_interface_empty_feature_collection():
+    geom = {"type": "FeatureCollection", "features": []}
+    feat = geom_obj(geom)
+
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        spec = alt.Chart(feat).mark_geoshape().to_dict()
+    assert spec["data"]["values"] == []
+
+
+# Features in a FeatureCollection only keep properties and geometry
+def test_geo_interface_feature_collection():
+    geom = {
+        "type": "FeatureCollection",
+        "features": [
+            {
+                "geometry": {
+                    "coordinates": [
+                        [[6.90, 53.48], [5.98, 51.85], [6.07, 53.51], [6.90, 53.48]]
+                    ],
+                    "type": "Polygon",
+                },
+                "id": 27,
+                "properties": {"type": "foo", "id": 1, "geometry": 1},
+                "type": "Feature",
+            },
+            {
+                "geometry": {
+                    "coordinates": [
+                        [[8.90, 53.48], [7.98, 51.85], [8.07, 53.51], [8.90, 53.48]]
+                    ],
+                    "type": "Polygon",
+                },
+                "id": 28,
+                "properties": {"type": "foo", "id": 2, "geometry": 1},
+                "type": "Feature",
+            },
+        ],
+    }
+    feat = geom_obj(geom)
+
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        spec = alt.Chart(feat).mark_geoshape().to_dict()
+    assert spec["data"]["values"][0]["id"] == 1
+    assert spec["data"]["values"][1]["id"] == 2
+    assert "coordinates" in spec["data"]["values"][0]["geometry"]
+    assert "coordinates" in spec["data"]["values"][1]["geometry"]
+    assert spec["data"]["values"][0]["type"] == "Feature"
+    assert spec["data"]["values"][1]["type"] == "Feature"
+
+
+# typical output of a __geo_interface__ from geopandas GeoDataFrame
+# notic that the index value is registerd as a commonly used identifier
+# with the name "id" (in this case 49). Similar to serialization of a
+# pandas DataFrame is the index not included in the output
+def test_geo_interface_feature_collection_gdf():
+    geom = {
+        "bbox": (19.89, -26.82, 29.43, -17.66),
+        "features": [
+            {
+                "bbox": (19.89, -26.82, 29.43, -17.66),
+                "geometry": {
+                    "coordinates": [
+                        [[6.90, 53.48], [5.98, 51.85], [6.07, 53.51], [6.90, 53.48]]
+                    ],
+                    "type": "Polygon",
+                },
+                "id": "49",
+                "properties": {
+                    "continent": "Africa",
+                    "gdp_md_est": 35900.0,
+                    "id": "BWA",
+                    "iso_a3": "BWA",
+                    "name": "Botswana",
+                    "pop_est": 2214858,
+                },
+                "type": "Feature",
+            }
+        ],
+        "type": "FeatureCollection",
+    }
+    feat = geom_obj(geom)
+
+    with alt.data_transformers.enable(consolidate_datasets=False):
+        spec = alt.Chart(feat).mark_geoshape().to_dict()
+    assert spec["data"]["values"][0]["id"] == "BWA"

.venv/Lib/site-packages/altair/vegalite/v4/tests/test_renderers.py

@@ -0,0 +1,65 @@
+"""Tests of various renderers"""
+
+import json
+
+import pytest
+
+import altair.vegalite.v4 as alt
+
+
+@pytest.fixture
+def chart():
+    return alt.Chart("data.csv").mark_point()
+
+
+def test_html_renderer_embed_options(chart, renderer="html"):
+    """Test that embed_options in renderer metadata are correctly manifest in html"""
+    # Short of parsing the javascript, it's difficult to parse out the
+    # actions. So we use string matching
+
+    def assert_has_options(chart, **opts):
+        html = chart._repr_mimebundle_(None, None)["text/html"]
+        for key, val in opts.items():
+            assert json.dumps({key: val})[1:-1] in html
+
+    with alt.renderers.enable(renderer):
+        assert_has_options(chart, mode="vega-lite")
+
+        with alt.renderers.enable(embed_options=dict(actions={"export": True})):
+            assert_has_options(chart, mode="vega-lite", actions={"export": True})
+
+        with alt.renderers.set_embed_options(actions=True):
+            assert_has_options(chart, mode="vega-lite", actions=True)
+
+
+def test_mimetype_renderer_embed_options(chart, renderer="mimetype"):
+    # check that metadata is passed appropriately
+    mimetype = alt.display.VEGALITE_MIME_TYPE
+    spec = chart.to_dict()
+    with alt.renderers.enable(renderer):
+        # Sanity check: no metadata specified
+        bundle, metadata = chart._repr_mimebundle_(None, None)
+        assert bundle[mimetype] == spec
+        assert metadata == {}
+        with alt.renderers.set_embed_options(actions=False):
+            bundle, metadata = chart._repr_mimebundle_(None, None)
+            assert set(bundle.keys()) == {mimetype, "text/plain"}
+            assert bundle[mimetype] == spec
+            assert metadata == {mimetype: {"embed_options": {"actions": False}}}
+
+
+def test_json_renderer_embed_options(chart, renderer="json"):
+    """Test that embed_options in renderer metadata are correctly manifest in html"""
+    mimetype = "application/json"
+    spec = chart.to_dict()
+    with alt.renderers.enable(renderer):
+        # Sanity check: no options specified
+        bundle, metadata = chart._repr_mimebundle_(None, None)
+        assert bundle[mimetype] == spec
+        assert metadata == {}
+
+        with alt.renderers.enable(option="foo"):
+            bundle, metadata = chart._repr_mimebundle_(None, None)
+            assert set(bundle.keys()) == {mimetype, "text/plain"}
+            assert bundle[mimetype] == spec
+            assert metadata == {mimetype: {"option": "foo"}}

.venv/Lib/site-packages/altair/vegalite/v4/tests/test_theme.py

@@ -0,0 +1,19 @@
+import pytest
+
+import altair.vegalite.v4 as alt
+from altair.vegalite.v4.theme import VEGA_THEMES
+
+
+@pytest.fixture
+def chart():
+    return alt.Chart("data.csv").mark_bar().encode(x="x:Q")
+
+
+def test_vega_themes(chart):
+    for theme in VEGA_THEMES:
+        with alt.themes.enable(theme):
+            dct = chart.to_dict()
+        assert dct["usermeta"] == {"embedOptions": {"theme": theme}}
+        assert dct["config"] == {
+            "view": {"continuousWidth": 400, "continuousHeight": 300}
+        }

.venv/Lib/site-packages/altair/vegalite/v4/theme.py

@@ -0,0 +1,56 @@
+"""Tools for enabling and registering chart themes"""
+
+from ...utils.theme import ThemeRegistry
+
+VEGA_THEMES = [
+    "ggplot2",
+    "quartz",
+    "vox",
+    "fivethirtyeight",
+    "dark",
+    "latimes",
+    "urbaninstitute",
+]
+
+
+class VegaTheme(object):
+    """Implementation of a builtin vega theme."""
+
+    def __init__(self, theme):
+        self.theme = theme
+
+    def __call__(self):
+        return {
+            "usermeta": {"embedOptions": {"theme": self.theme}},
+            "config": {"view": {"continuousWidth": 400, "continuousHeight": 300}},
+        }
+
+    def __repr__(self):
+        return "VegaTheme({!r})".format(self.theme)
+
+
+# The entry point group that can be used by other packages to declare other
+# renderers that will be auto-detected. Explicit registration is also
+# allowed by the PluginRegistery API.
+ENTRY_POINT_GROUP = "altair.vegalite.v4.theme"  # type: str
+themes = ThemeRegistry(entry_point_group=ENTRY_POINT_GROUP)
+
+themes.register(
+    "default",
+    lambda: {"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}},
+)
+themes.register(
+    "opaque",
+    lambda: {
+        "config": {
+            "background": "white",
+            "view": {"continuousWidth": 400, "continuousHeight": 300},
+        }
+    },
+)
+themes.register("none", lambda: {})
+
+for theme in VEGA_THEMES:
+    themes.register(theme, VegaTheme(theme))
+
+themes.enable("default")